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We consider multi-choice cooperative games with a permission tree structure. Multi-choice games are a generalization of cooperative transferable utility games in which each player has several activity levels. In addition, a permission tree structure models a situation in which a player needs permission from another player to cooperate. In this framework, the influence of a permission structure on the possibility of cooperation may have several interpretations depending on the context. In this paper, we investigate several of these interpretations and introduce for each of them a new allocation rule that we axiomatically characterize.

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is via the Dynamic Programming principle. Nevertheless, despite many theoretical results, this method has been applied only to very special cases since it suffers from the curse of dimensionality . Our goal is to mitigate this crucial obstruction developing a version of dynamic programming algorithms based on a tree structure and exploiting the compact representation of the dynamical systems based on tensors notations via a model reduction approach. Here, we want to show how this algorithm can be constructed for general nonlinear control problems and to illustrate its performances on a number of challenging numerical tests introducing novel pruning strategies that improve the efficacy of the method. Our numerical results indicate a large decrease in memory requirements, as well as computational time, for the proposed problems. Moreover, we prove the convergence of the algorithm and give some hints on its implementation.

Conifer tree rings are generally composed of large, thin-walled cells of light earlywood followed by narrow, thick-walled cells of dense latewood. Yet, how wood formation processes and the associated kinetics create this typical pattern remains poorly understood. We monitored tree-ring formation weekly over 3 yr in 45 trees of three conifer species in France. Data were used to model cell development kinetics, and to attribute the relative importance of the duration and rate of cell enlargement and cell wall deposition on tree-ring structure. Cell enlargement duration contributed to 75% of changes in cell diameter along the tree rings. Remarkably, the amount of wall material per cell was quite constant along the rings. Consequently, and in contrast with widespread belief, changes in cell wall thickness were not principally attributed to the duration and rate of wall deposition (33%), but rather to the changes in cell size (67%). Cell enlargement duration, as the main driver of cell size and wall thickness, contributed to 56% of wood density variation along the rings. This mechanistic framework now forms the basis for unraveling how environmental stresses trigger deviations (e. g. false rings) from the normal tree-ring structure

This paper explores branching vocabulary items as a possible alternative to backtracking in Nanosyntax. Such vocabulary items give rise to the possibility of partial overwrite. Initially, they spell out a node with two phrasal daughters. At a subsequent stage, the right branch is interpreted by a new element, but the original item continues to spell out the left branch of such a derivation. The effects that branching vocabulary items give rise to thus mimic aspects of backtracking, without requiring mechanisms that undo parts of the derivation.

Information-centric networking (ICN) shifts the communication model from a host-centric paradigm to an information-centric paradigm, and is promising for solving several problems on today’s Internet. For more efficient information dissemination, most ICN architectures are based on the Identifier/Locator split design. Therefore, how to map an identifier to a routable locator is an important problem for efficient data transmission. Nowadays, many new network services such as industrial control and telemedicine are highly latency-sensitive and require deterministic service response latency. To meet such requirements, name resolution with a deterministic latency guarantee is needed, but less discussed. This paper proposes a tree-based resolution system structure for deterministic latency resolution, which can support the Local Name Mapping Resolution System (LNMRS) in the new ICN network architecture—SEANet—to provide deterministic name resolution service in latency-sensitive scenarios like industrial control and telemedicine. The correctness of such a structure is the key to achieving deterministic latency resolution. To ensure the structure’s correctness in a distributed manner, a tree structure protocol based on delay measurement is also proposed for structure generation and maintenance. Simulation results show that the protocol is effective in generating a correct structure that has good performance in terms of service capability for deterministic name resolution and system scalability.

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

Tree-like architectures and branching structures are one of the analogical designs that are among the nature inspired structures arousing attention of the designers, inspiring them and that are frequently confronted throughout the history of architecture. Likewise, trees are structural models for designers beyond the plant and branching patterns that are used as architectural ornamentation. Trees have the characteristics of being mentors for architects and engineers concerning how the vertical and the horizontal loads are transmitted through the trunks, branches, and leaves and how the balance is provided. Within this context, it is possible to claim that a quite wide and intuitively developed structural knowledge is acquired with the tree analogies throughout the history of architecture. By the development of computational design technologies, there have been significant developments in the design and the building of tree-like structures. Especially the number of branching, angles of dendroids, lengths, and the other parameters can be defined by using algorithms and can be optimized also by the help of computational tools. In this paper, the historical development and classification of the tree-like structures have been carried out and Frei Otto who is the pioneer to pave the way for innovative structures related to this field has been selected to revisit the efficiency of lightweight columns inspired by nature. One of the experimental studies of Otto in which he called as “minimum path system” has been determined as the case study; the parametric design behind the structure has been analyzed and reproduced by using a parametric software. The structural effectiveness has been determined and discussed by testing the obtained models using a FEM program under horizontal and vertical loads. Consequently, the structural effectiveness of today’s computational technologies and the branching structures that Otto built intuitively and by natural analogies have been able to be tested and the possible potentials that can be leading for today’s architects have been demonstrated.

Multiple-point statistics are widely used for the simulation of categorical variables because the method allows for integrating a conceptual model via a training image and then simulating complex heterogeneous fields. The multiple-point statistics inferred from the training image can be stored in several ways. The tree structure used in classical implementations has the advantage of being efficient in terms of CPU time, but is very RAM demanding and then implies limitations on the size of the template, which serves to make a proper reproduction of complex structures difficult. Another technique consists in storing the multiple-point statistics in lists. This alternative requires much less memory and allows for a straightforward parallel algorithm. Nevertheless, the list structure does not benefit from the shortcuts given by the branches of the tree for retrieving the multiple-point statistics. Hence, a serial algorithm based on list structure is generally slower than a tree-based algorithm. In this paper, a new approach using both list and tree structures is proposed. The idea is to index the lists by trees of reduced size: the leaves of the tree correspond to distinct sublists that constitute a partition of the entire list. The size of the indexing tree can be controlled, and then the resulting algorithm keeps memory requirements low while efficiency in terms of CPU time is significantly improved. Moreover, this new method benefits from the parallelization of the list approach.

Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.